Methodology for extracting effective lens aberrations using a neural network

ABSTRACT

A method ( 250 ) of extracting effective imaging system aberrations from test data collected from test structures ( 220 ) constructed from a lithography system having an imaging system associated therewith includes inputting ( 264 ) experimental critical dimension data corresponding to fabricated features ( 220 ) on a substrate ( 212 ) to a neural network ( 208 ). The method ( 250 ) also includes inputting ( 266 ) nominal critical dimension data corresponding to the fabricated features on the substrate ( 212 ) to the neural network ( 208 ) and determining ( 268 ) the effective aberrations of the imaging system associated with the lithography system used to fabricate the features ( 220 ) using the neural network ( 208 ).

FIELD OF THE INVENTION

The present invention generally relates to photolithography, and moreparticularly relates to a system and method for characterizing animaging system within a projection photolithography system and providingillumination compensation in response to such a characterization.

BACKGROUND OF THE INVENTION

Lithography in semiconductor processing relates generally to the processof transferring patterns which correspond to desired circuit componentsonto one or more thin films which overlie a substrate. One importantstep within the field of lithography involves optical tools and methodsfor transferring the patterns to the films which overlie thesemiconductor wafer. Patterns are transferred to a film by imagingvarious circuit patterns onto a photoresist layer which overlies thefilm on the wafer. This imaging process is often referred to as“exposing” the photoresist layer. The benefit of the exposure processand subsequent processing allows for the generation of the desiredpatterns onto the film on the semiconductor wafer, as illustrated inprior art FIGS. 1a-1 f.

Prior art FIG. 1a illustrates a photoresist layer 10 deposited by, forexample, spin-coating, on a thin film 11 such as, for example, silicondioxide (SiO₂) which overlies a substrate 12 such as silicon. Thephotoresist layer 10 is then selectively exposed to radiation 13 (e.g.,ultraviolet (UV) light) via a photomask 14 (hereinafter referred to as a“mask”) to generate one or more exposed regions 16 in the photoresistlayer 10, as illustrated in prior art FIG. 1b. Depending on the type ofphotoresist material utilized for the photoresist layer 10, the exposedregions 16 become soluble or insoluble in a specific solvent which issubsequently applied across the wafer (this solvent is often referred toas a developer).

The exposed regions 16 are made either soluble or insoluble in thedeveloper. When the exposed regions 16 are made soluble, a positiveimage of the mask 14 is produced in the photoresist layer 10, asillustrated in prior art FIG. 1c, and the photoresist material istherefore referred to as a “positive photoresist”. The exposedunderlying areas 18 in the film 11 may then be subjected to furtherprocessing (e.g., etching) to thereby transfer the desired pattern fromthe mask 14 to the film 11, as illustrated in prior art FIG. 1d (whereinthe photoresist layer 10 has been removed). Conversely, when the exposedregions 16 are mode insoluble, a negative image of the mask 14 isproduced in the photoresist 10 layer, as illustrated in prior art FIG.1e, and the photoresist material is therefore referred to as a “negativephotoresist”. In a similar manner, the exposed underlying areas 20 inthe film 11 may then be subjected to further processing (e.g., etching)to thereby transfer the desired pattern from the mask 14 to the film 11,as illustrated in prior art FIG. 1f.

The transfer of patterns to the photoresist layer 10 as discussed aboveinvolves the use of optical aligners. Optical aligners are machineswhich contain a variety of subsystems that work together to form theimaging function. Such optical aligners include: (1) an illuminationsource which provides the optical energy (UV light in the above example)for transforming the photoresist via exposure, (2) an optical subsystemthat focuses the circuit patterns onto the photoresist surface andallows for controlled exposure times, and (3) and a movable stage thatholds the wafer being exposed.

Historically, three primary methods have been used to optically transfera mask pattern to a photoresist covered film. These methods are: contactprinting, proximity printing and projection printing and are illustratedin simplified form in prior art FIGS. 2a-2 d, respectively. Contactprinting 100, as illustrated in prior art FIG. 2a, was the earliestmethod used to produce patterns. Contact printing 100 involves a lightsource 112, an optical system 114, a mask 116 and a photoresist layer118 overlying a thin film 119 (not shown) which, in turn, overlies asemiconductor wafer 120. The mask 116, which contains the desiredcircuit patterns for transfer to the photoresist layer 118, ispositioned (aligned) relative to any existing patterns that alreadyexisted on the wafer 120. The mask 116 is then clamped down to thephotoresist layer 118, thereby making physical contact with thephotoresist layer 118, and exposed with ultraviolet (UV) light from thelight source 112. This method provides for an excellent image transferand good resolution (i.e., good minimum linewidth spacing).

Contact printing, however, suffers from the direct contact made betweenthe mask 116 and the photoresist layer 118. The repeated contact madebetween the mask 116 and the photoresist layer 118 in the processresults in defects generated in the mask 116 which are then reflected inthe transfer made on subsequently processed wafers. To prevent thisproblem, the masks 116 must be disadvantageously inspected and cleanedregularly. In addition, small particles may be caught between the mask116 and the photoresist layer 118 when affixing the two elements,thereby preventing the desired direct contact between the mask 116 andthe photoresist layer 118. This particulate contamination results inreduced resolution in the area local to the foreign particle.Consequently, contact printing is not common in VLSI semiconductormanufacturing.

Proximity printing 122, as illustrated in prior art FIG. 2b, involvesplacing the mask 116 near the wafer 120 (which is covered with thephotoresist 118) during exposure, however, the mask 116 and the wafer120 do not make contact. By introducing a gap 124 between the mask 116and the wafer 120, the defect problem of contact printing issubstantially avoided. Unfortunately, as the gap 124 increases, theresolution of the proximity printing system 122 rapidly deteriorates.For example, a 10 μm gap with a 400 nm exposure (the wavelength of thelight source 112) results in a minimum resolution of about 3 μm. Inaddition, proximity printing 122 requires extremely flat masks 116 andwafers 120 in order to prevent gap variations spatially about the wafer120. Since many VLSI semiconductor circuits today require features of0.25 μm or less, proximity printing 122 is not considered adequate formany VLSI semiconductor manufacturing operations.

Projection printing is a generic term that encompasses various patterntransfer techniques. These techniques, for example, include: (a)projection scanning systems, (b) reduction (e.g., 4X or 10X)step-and-repeat projection systems and (c) reduction step-and-scansystems. In each system, lens elements or mirrors are used to focus themask image on the wafer surface (containing the photoresist).

Projection scanning systems (often called scanning projection aligners),use a reflective spherical mirror (reflective optics) to project animage onto the wafer surface, as illustrated, for example, in prior artFIG. 2c. The system 126 includes a primary mirror 128 and a secondarymirror 129 which are arranged with the mask 116 and the wafer 120 toimage the mask pattern onto the photoresist layer 118 which overlies thefilm 119 on the wafer 120 (the photoresist layer 118 and the thin film119 are not shown in FIG. 2c for simplicity). A narrow arc of radiationis imaged from the mask 116 to the wafer 120 with light that travels anoptical path that reflects the light multiple times. The mask 116 andthe wafer 120 are scanned through the arc of radiation by means of acontinuous scanning mechanism (not shown). The scanning techniqueminimizes mirror distortions and aberrations by keeping the imagingillumination in the “sweet spot” of the imaging system 128 and 129.

Reduction step-and-repeat systems 130 (also called reduction steppers)use refractive optics (as opposed to reflective optics in the system 126of prior art FIG. 2c) to project the mask image onto the photoresistlayer 118 which overlies the film 119 on the wafer 120, as illustrated,for example, in prior art FIG. 2d. The reduction stepper 130 includes amirror 132, a light source 134, a filter 136, a condenser lens system138, a reticle 140, a reduction lens system 142 and the wafer 120. Themirror 132 behaves as a collecting optics system to direct as much ofthe light from the light source 134 (e.g., a mercury-vapor lamp) to thewafer 120. The filter 136 is used to limit the light exposurewavelengths to the specified frequencies and bandwidth. The condensersystem 138 focuses the radiation through the reticle 140 and to thereduction lens system 142 to thereby focus a “masked” radiation exposureonto a limited portion of the wafer 120, namely onto a singlesemiconductor die 144.

Since it is complex and expensive to produce a lens capable ofprojecting a mask pattern of an entire 150 mm or 200 mm wafer, therefractive system 130, as illustrated in prior art FIG. 2d, projects animage only onto a portion of the wafer 120 corresponding to anindividual semiconductor die 144. This image is then stepped andrepeated across the wafer 120 in order to transfer the pattern to theentire wafer (and thus the name “steppers”). Consequently, the size ofthe wafer is no longer a consideration for the system optics. Inaddition, the field of view may be scanned in order to utilize thecenter of the lens. These systems are referred to as step and scansystems.

The reduction stepper system 130 thus uses the reticle 140 instead of amask. Reticles are similar to masks, but differ in that a mask containsa pattern for transfer to the entire wafer in one exposure while areticle contains a pattern image for a single or several semiconductordie that must be stepped and repeated across the wafer 120 in order toexpose the entire wafer substrate. In modern systems, however, the termsreticle and mask are used interchangably. Current reduction steppersystems such as the system 130 utilize reticles that contain a patternthat is an enlargement of the desired image on the wafer 120.Consequently, the reticle pattern is reduced when projected onto thewafer 120 during exposure (and thus the name “reduction stepper”).

One advantage of stepper technology over the full wafer scanning typetechnology is higher image resolution (i.e., smaller minimumlinewidths). In addition, stepping each die on the wafer 20 allowscompensation for wafer distortion. Further still, reduction steppersprovide good overlay accuracy. Steppers do, however, exhibit reducedthroughput (number of wafers/hour) and require precision control of themechanical stage (not shown) which holds the wafer 120. The advantagesof reduction steppers, however, presently outweigh their disadvantagesand thereby make reduction steppers quite popular in the manufacture ofVLSI semiconductors with minimum linewidths less than 1 μm.

Although projection-type lithography systems provide good linewidthcontrol, future generation lithography requires continuing improvementsin linewidth, feature resolution, and critical dimension control both ona die-to-die basis and within a single die. Providing such advancedcritical dimension control across a single die, however, is difficultbecause of a number of variations which may exist within the lithographysystem. For example, various types of aberrations may exist within animaging system (primarily consisting of lens aberrations), which createcorresponding critical dimension non-uniformities of features across thewafer. Detailed characterization information regarding such lensaberrations, however, is not provided by lithography system vendors;rather such imaging systems are merely guaranteed to meet a minimumimaging performance specification. Consequently, because lenscharacterization information is not known for a particular lithographysystem, the manner and extent to which critical dimensionnon-uniformities are manifest across a single die is also unknown.

Therefore there is a need in the art for a system and method ofcharacterizing an imaging system within a projection photolithographysystem.

SUMMARY OF THE INVENTION

The present invention relates to a system and method for characterizingan imaging system within a projection-type photolithography system andproviding compensation in response to such characterization to therebyimprove critical dimension control across the image field.

According to one aspect of the present invention, a system and methodfor characterizing the imaging system includes use of a neural networkor other type of intelligent or expert system. A plurality ofexperimental test features are formed at a plurality of points on asubstrate using the imaging system to be characterized, which correspondto a plurality of points in the image field. The critical dimensions ofthe experimental test features are then measured for future processing.

In addition, a lithography simulator is utilized to characterize thedeviation in the critical dimensions of simulated features from nominalfeature critical dimensions for a variety of imaging system aberrationconditions. The simulation data is saved and utilized as a training setto calibrate the neural network and thus establish a transfer functionwhich maps a critical dimension variation for a feature from its nominalcritical dimension to an effective imaging system aberration condition.Once trained, the neural network is used to characterize the imagingsystem using the experimental test feature critical dimension data atthe plurality of points and the nominal feature critical dimension dataassociated therewith. Using such input data, the calibrated neuralnetwork identifies an effective imaging system aberration condition atvarious points in the image field which could produce the notedvariation between the experimental and nominal feature criticaldimensions.

According to another aspect of the present invention, a system andmethod of compensating for imaging system aberrations is provided toimprove critical dimension uniformity across the image field. Imagingsystem characterization information is utilized in conjunction with alithography simulator to identify a customized illumination scheme whichcompensates for the imaging system aberrations. An output of thelithography simulator representing simulated feature critical dimensionsat points across the image field is compared to nominal feature criticaldimension data associated with the points and an input illuminationscheme for the lithography simulation is modified in response thereto.

Once the new illumination scheme is generated, another simulation isprovided using the new illumination scheme, thus resulting in newsimulated feature critical dimension data at the plurality of points.The comparison with the nominal feature critical dimension data and anillumination scheme modification in response thereto is continued untilthe difference between the simulated and nominal feature criticaldimension data is approximately the same. The resulting customillumination scheme associated therewith is then utilized in conjunctionwith the characterized imaging system to compensate for the imagingsystem aberration conditions.

To the accomplishment of the foregoing and related ends, the inventioncomprises the features hereinafter fully described and particularlypointed out in the claims. The following description and the annexeddrawings set forth in detail certain illustrative embodiments of theinvention. These embodiments are indicative, however, of but a few ofthe various ways in which the principles of the invention may beemployed. Other objects, advantages and novel features of the inventionwill become apparent from the following detailed description of theinvention when considered in conjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1a is a fragmentary cross section illustrating a prior artsemiconductor substrate having a film overlying the substrate which inturn is covered by a photoresist layer;

FIG. 1b is a fragmentary cross section illustrating a prior art methodof selectively exposing a photoresist layer using a mask;

FIG. 1c is a fragmentary cross section illustrating a positivephotoresist layer after being developed.

FIG. 1d is a fragmentary cross section illustrating a transfer of a maskpattern to the film;

FIG. 1e is a fragmentary cross section illustrating a negativephotoresist layer after being developed;

FIG. 1f is a fragmentary cross section illustrating a transfer of a maskpattern to the film;

FIG. 2a is a cross section system view of a prior art contact printingsystem wherein a mask physically contacts the wafer during transfer of apattern to the wafer;

FIG. 2b is a cross section system view of a prior art proximity printingsystem wherein a mask is separated from the underlying wafer by a gapduring transfer of a pattern to the wafer;

FIG. 2c is a cross section system view of a prior art scanningprojection system wherein reflective optics are used to transfer apattern to the wafer.

FIG. 2d is a perspective system view of a prior art projection-typereduction stepper wherein refractive optics are used to transfer apattern to a localized region on the wafer;

FIG. 3 is a block diagram illustrating a system for extracting effectiveimaging system aberration information from a lithography imaging systemaccording to the present invention;

FIG. 4 is a flow chart diagram illustrating a method of extractingeffective imaging system aberration information from a lithographyimaging system according to the present invention;

FIG. 5 is a plan view of a semiconductor die having a plurality ofregions thereon, wherein each of the regions contain a plurality ofexperimental test features which are fabricated by the lithographysystem to be characterized;

FIG. 6 is a plan view of the plurality of experimental test features ofFIG. 5 according to an exemplary embodiment of the present invention;

FIG. 7 is a block diagram illustrating a lithography simulator forsimulating nominal features which correspond to the experimental testfeatures of FIGS. 5 and 6 for a plurality of imaging system aberrationconditions;

FIG. 8 is a block diagram illustrating a training or calibration of aneural network using a training set according to the present invention;

FIG. 9 is a schematic diagram illustrating a simplified, exemplaryneural network for use in extracting effective aberrations of alithography imaging system according to the present invention;

FIG. 10 is a flow chart diagram illustrating a method of training orcalibrating the neural network using a training set according to thepresent invention;

FIG. 11 is a block diagram illustrating use of a trained or calibratedneural network using the experimental test feature critical dimensiondata and nominal feature critical dimension data to determine effectiveaberration conditions of the imaging system at various points accordingto the present invention.

FIG. 12 is a block diagram illustrating a system for generating acustomized illumination scheme to compensate for aberrations associatedwith a lithography imaging system according to the present invention;

FIG. 13 is a block diagram illustrating a lithography simulatoraccording to the present invention;

FIG. 14 is a flow chart diagram illustrating a method of generating acustomized illumination scheme to compensate for aberrations associatedwith a lithography imaging system according to the present invention;

FIG. 15 is a perspective view of an exemplary projection-typelithography system having an illumination scheme modification deviceaccording to the present invention; and

FIG. 16 is a plan view of an exemplary illumination scheme modificationdevice having a plurality of pupil filters thereon according to thepresent invention.

DETAILED DESCRIPTION OF THE INVENTION

The following is a detailed description of the present invention made inconjunction with the attached Figures, wherein like reference numeralswill refer to like elements throughout. The present invention includes asystem and method for characterizing an imaging system in aprojection-type photolithography system. The characterizationinformation is then used to generate a custom illumination scheme foruse with the imaging system which compensates for aberrations thereinand thus improves the critical dimension uniformity of features formedacross a substrate which is processed with the characterizedphotolithography system.

According to one aspect of the present invention, a system and method isdisclosed which characterizes an imaging system at a plurality of pointsin the image field. A plurality of experimental test features are formedon a substrate at a plurality of locations using the imaging system tobe characterized, and the critical dimensions of the features aremeasured and stored as a set of experimental feature critical dimensionsfor the plurality of locations.

Separately, a number of lithography simulations are conducted by fixingthe lithography processing conditions and a plurality of nominalsimulation input features while varying the type of imaging systemaberrations. The output lithography simulation data provides informationregarding how the particular imaging system aberration condition causesa deviation in the critical dimensions of the simulated features fromthe intended nominal feature critical dimensions. This data is then usedas a training set to calibrate a neutral network and thus establish atransfer function which maps an effective imaging system aberrationcondition to a given set of critical dimension data at a given point.After calibration, the neutral network is used to identify an effectiveimaging system aberration at a given point in the image field byanalyzing the difference between the experimental test feature criticaldimensions and the expected nominal feature critical dimension at thatpoint.

According to another aspect of the present invention, imaging systemcharacterization data is used to establish a custom illumination schemewhich compensates for imaging system aberrations, thus providingincreased feature critical dimension uniformity across a substrate.Preferably, an initial illumination scheme is used along with theimaging system characterization data to perform a lithographysimulation, thus resulting in simulated feature critical dimension dataat a plurality of points. The simulated feature critical dimension datais then analyzed and/or compared with the expected nominal featurecritical dimensions at the plurality of points, an illumination schemeis modified in response to the comparison and another lithographysimulation is run with the modified illumination scheme. The steps ofanalyzing the output data and modifying the illumination scheme inresponse thereto is repeated until the simulated feature criticaldimensions are within an acceptable range of the expected nominalfeature critical dimensions. The identified custom illumination schemeis then utilized in conjunction with the characterized imaging system toprovide compensation for the aberrations within the imaging system andthus provides improved feature critical dimensions uniformity across thesubstrate.

Turning now to the Figures, FIG. 3 is a system level block diagramillustrating a system 200 for characterizing an imaging system within aprojection-type photolithography system. According to a preferredembodiment of the present invention, the imaging system includes a lensand a series of mirrors, etc. which aid in the imaging of a patternresiding on a photomask onto a substrate. Typically, the primary defectsor aberrations in the imaging system are due to the lens and result incritical dimension non-uniformities across the imaged pattern on thesubstrate. The system 200 includes a lithography simulator 202 which mayinclude, for example, a personal computer or workstation utilizinglithography simulation software such as Prolith/2 or Solid-C,manufactured by Finle Technologies, P.O. Box 162712, Austin, Tex. 78716and Sigma-C, GmbH, Munich, Germany, respectively.

The lithography simulator 202 is operatively coupled to a memory 204 isstore simulation data from the lithography simulator 202. The memory 204may include a memory within a computer such as a RAM, a computer harddrive, or a centralized shared storage device; alternatively, the memory204 may include a portable memory media such as a magnetic diskette or atape, etc. Any memory structure capable of storing the lithographysimulation data may be used and is contemplated at falling within thescope of the present invention. The system 200 of FIG. 3 furtherincludes a neural network 206 which is operatively coupled to the memory204. As will be described in greater detail infra, the neural network206 uses the lithography simulation data within the memory 204 as atraining set to calibrate the neural network 206 and thereby establish acalibrated neural network 208 having a transfer function which isoperable to map a deviation between an experimental test featurecritical dimension and a nominal feature critical dimension to aneffective imaging system aberration which could have produced thevariation.

Another memory 210 is operatively coupled to the calibrated neuralnetwork 208. The memory 210 contains experimental test feature criticaldimension data for a plurality of points across a substrate 212 whichwas processed using the imaging system being characterized. The memory210 may be separate from the memory 204 or alternatively may beintegrated with the memory 204, as may be desired.

A method 250 in which the imaging system characterization is effectuatedis illustrated in FIG. 4 and the method 250 will be further explained inconjunction with FIGS. 3 and 5-11. The method 250 begins at Step 252with the generation of a plurality of experimental test features acrossat least a portion of the substrate 212 (e.g., a die on the substrate212) which corresponds with a plurality of points across the image fieldof the imaging system. The experimental test features are generatedaccording to any number of conventional means. For example, the featuresmay be an exposed a developed photoresist pattern or, alternatively, maybe a patterned film such as doped polysilicon, a metal or aninsulating-type film.

Preferably, the experimental test features consist of patterned, dopedpolysilicon which is conductive and thus is capable of beingsubstantially measured using electrical linewidth measurementtechniques. The experimental test features are preferably formed bydepositing the polysilicon by, for example, chemical vapor deposition(CVD), and then doping the polysilicon by, for example, ion implantationto increase the conductivity of the polysilicon. A photoresist film isthen formed over the polysilicon via, for example, spin-coating, and isthen exposed and developed to expose portions of doped polysiliconunderneath. The exposed polysilicon is then etched using, for example, aselected etching plasma chemistry to thereby form the experimental testfeatures.

Preferably, the experimental test features of interest are on a singledie 216 in an array, as illustrated in FIGS. 5 and 6. The die 216 has aplurality of points 218, arranged in a grid and shown as points (X₁,X_(Y) ₁), (X₂, Y₂), . . . (X₁, Y₁) in FIG. 5. Each of the points 218correspond to a point in the image field being characterized. Accordingto a preferred embodiment of the present invention, a plurality ofexperimental test features are formed in each of the points 218, whereineach of the features will have critical dimensions (i.e., {tilde over(CD)}₁,{tilde over (CD)}₂, . . .{tilde over (CD)}_(n), for “n” featuresat each point 218). Since various types of features exhibit differenttypes of critical dimension variations from their nominal or expectedcritical dimensions for a given imaging system aberration condition,utilizing more than one type of feature at each point 218 is desirableand thus preferred to help more fully characterize the imaging systemaberration at any given point in the image field.

An exemplary plurality of experimental test features 220 is illustratedin FIG. 6 As is well known by those skilled in the art, a “feature” istypically characterized using its nominal critical dimension and itsline/space ratio with respect to neighboring structures. Consequently, aline having a 0.25 micron dimension exhibiting a 1:1 line/space ratiomay appear as Feature 1 of FIG. 6 while another line exhibiting the same0.25 micron dimension which is isolated from neighboring structures(such as Feature 2 of FIG. 6) is considered a different type of feature.Similarly, another structure having a line dimension of 0.25 micron witha line/space ratio of 1:2 (see FIG. 3 of FIG. 6) is considered yetanother type of feature. Therefore for each point 218 on the die 216, aplurality of features 220 having differing dimensions and line/spaceratios are preferably utilized to more fully and accurately characterizethe aberration at a given point.

Returning now to FIG. 4, the method 250 continues at Step 254, whereineach of the experimental test features 220 are preferably measured ateach of the plurality points 218. The measurement of the features may beaccomplished via a variety of conventional methods such as with anelectrical linewidth measurement (ELM) technique, optical techniques(e.g., laser, electron beam or ion beam measurements), use of thescanning electron microscope (SEM), etc. Preferably, however, ELM isused. Since “n” experimental test features exist at each point 218 ofthe 216, the critical dimension data at each point will be ({tilde over(CD)}₁, X₁, Y₁), ({tilde over (CD)}₂,X₁, Y₁). . . ({tilde over(CD)}_(n), X₁,Y₁). ({tilde over (CD)}₁,X_(i),Y_(j)), ({tilde over(CD)}₂X_(i),Y_(j)), . . . ({tilde over (CD)}_(n), X_(i),Y_(j))). Oncethe experimental test features are measured, the experimental testfeature critical dimension data is stored in the memory 210 of FIG. 3 atStep 256.

The method 250 continues at Step 258 by performing a plurality oflithography simulations, as illustrated in FIG. 7. Preferably, thelithography simulator 202 used to perform the lithography simulations isa lithography simulation software package such as Prolith/2 or Solid-Coperated on a personal computer workstation, however, other lithographysimulation software or other type simulation techniques may be utilizedand any such alternative methods and techniques are contemplated asfalling within the scope of the present invention. In performing thelithography simulations, it is preferred that a given set of lithographyprocess conditions (e.g., illumination dose, focus, equipment setupdata, etc.) be fixed at the processing conditions which were used in thegeneration of the experimental test features at Step 252. According toan exemplary embodiment of the present invention, a dose is about 12mJ/cm², a focus is 0 (e.g., best focus), a resist thickness is 0.75micron, and a post-exposure back is 100° C. for 110 seconds, etc.

In addition, the lithography simulator 202 utilizes, as inputs, nominalfeature critical dimension data for a plurality of nominal featuresincluding the linewidth dimension and line/space ratio. The nominalfeatures of the simulation Step 258 correspond to the intended featurecritical dimensions of the experimental test features at a single point218 that were generated in Step 252. Then, for the given lithographyprocess conditions and the specified nominal features, an imaging systemaberration condition is provided which reflects a potential aberrationwhich may exist within the imaging system. Such an aberration conditionis preferably characterized as a set of Zerniike coefficients (Z₁,Z₂, .. . Z_(p)) for a polynomial. As is well known by those skilled in theart, Zernike polynomials are circle polynomials which may be used todescribe the aberrations of an optical system. Such polynomials may beclassified as standard polynomials or as so-called Fringe code, as maybe desired. The output of the lithography simulation that is run for thegiven imaging system aberration condition (along with the input data) issaved in the memory 204 at Step 260. The simulation output consists ofsimulated feature critical dimensions ({overscore (CD)}, {overscore(CD)}₂, . . . {overscore (CD)}_(n)), which correspond to the inputnominal features (CD₁,CD₂, . . . CD_(n)). Note that some or all of thecritical dimensions of the simulated output features will vary from thecritical dimensions of the intended nominal features due to the imagingsystem aberration condition (e.g., {overscore (CD)}₁=CD, ±ΔCD₁).

Once the lithography simulation is completed for a first imaging systemaberration condition, another aberration condition is input to thelithography simulator (e.g., a different set of Zernike coefficients),another simulation is run and the output data (along with its inputdata) is saved in the memory 204. This process (Steps 258 and 260) ispreferably repeated until all of the different imaging system aberrationconditions have been simulated. One exemplary way of accomplishing theabove steps is as follows. If each imaging system aberration conditionis expressed as a polynomial having “P” Zernike coefficients, and eachcoefficient can take on one of “Q” different values, then the totalnumber of potential imaging system aberration conditions is equal toP×Q. Therefore, the P×Q different imaging system aberration conditionsare preferably used to perform P×Q lithography simulations (Step 258),each of which has its data saved (Step 260) in order to have sufficientdata to fully identify the various imaging system aberrations, as willbe discussed in greater detail infra.

The simulation data in the memory 204 is then used as a training set tocalibrate the neural network 206 at Step 262, as illustrated in FIGS. 4and 8-9. As illustrated in FIG. 8, the neural network 206 is configuredto receive, as an input vector, the critical dimensions of the nominalfeatures (CD,,CD₂, . . . CD_(n)) and the critical dimensions of thesimulated features ({overscore (CD)}₁,{overscore (CD)}₂,. . . {overscore(CD)}_(n)) and provide an output vector which represents the imagingsystem aberration condition which produces the variations between theintended nominal features and the simulated features (e.g., a set ofZernike coefficients, {overscore (Z)}₁, {overscore (Z)}₂, . . .{overscore (Z)}_(p)). One manner in which the neural network 206 may betrained will be discussed in conjunction with FIGS. 9 and 10.

FIG. 9 is an exemplary illustration of the neural network 206 arrangedas a multi-layer perceptron 280. The perceptron 280 includes a pluralityof layers 282 having neurons 284 which, in hardware arrangements, mayconstitute individual processors such as an op-amp, etc. and, insoftware arrangements, may constitute algorithmic connection nodes. Eachneuron 284 which preferably produces an output which is a predeterminedfunction of its inputs. While only three neurons are shown in each layer282 in FIG. 9, it should be understood that any number of neurons 284may be employed depending on the number of features at a given point. Inaddition, the number of layers 282 may also vary, as may be desired orneeded. Each neuron 284 within each layer 282 is preferably connected toeach neuron of each adjacent layer.

Each of the connections 286 between the neurons 284 contain weights orsynapses 288. The synapses 288 may be implemented in hardwareimplementations via variable resistances or amplifiers having variablegains or may, in software implementations, simply include fractionalweights for modifying a neuron function. The synapses 288 serve toreduce or increase the strength of the connection between the neurons284. The value of the connection strengths of each synapse 288 may varyfrom some predetermined maximum value (typically “1”) to zero. When theweight is zero there is, in effect, no connection between the neurons284.

The process of training or calibrating the neural network 206 involvesadjusting the connection strengths of each synapse 288 in a repetitivefashion until a desired output vector is produced in response to aparticular input vector. In particular, the training of the neuralnetwork 206 will have the strength of the synapses 288 adjusted toproduce an output reflecting the imaging system aberration condition(Zernike coefficients) associated with the inputs which reflect thedeviation between the nominal feature critical dimensions and thesimulated feature critical dimensions. Since the training set knows whataberration condition is associated with the input data (due to theearlier simulations), the neural network output is compared to theexpected output and the weighting values of the synapses 288 aremodified in response thereto. This process is then reiterated multipletimes till the neural network output is within an acceptable range ofthe expected output (e.g., about ±5% or less), as illustrated in atraining method 290 of FIG. 10.

Typically such a training procedure 290 analyzes the output aberration(Step 292) (e.g., Zernike coefficients associated with the simulationswhich produced such critical dimension variations, Z₁,Z₂, . . . . Z_(p))and compares the output from the expected output (Step 294) and basedupon the difference between the two (Step 296), either the procedureends and the neural network 206 is calibrated (Step 298) or an errorsignal is produced which is used in accordance with a training algorithmto adjust the weights of the various synapses 288 to reduce the value ofthe subsequent output error (Step 300). A multitude of differenttraining algorithm techniques may be utilized and any such technique iscontemplated as falling within the scope of the present invention. Forexample, a back propagation training algorithm or a Levenberg-Marquardtalgorithm may be utilized. Please note, however, that numerous differenttraining algorithms exist and any such training algorithm iscontemplated as falling within the scope of the present invention.

According to a preferred embodiment of the present invention, a neuralnetwork 206 is calibrated using a training set to form a neural network208 having a transfer function which maps deviations from nominalfeature critical dimension data to an effective imaging systemaberration condition which could have generated the noted deviation. Aneural network is the preferred mechanism for determining suchaberrations because it is not necessary for an individual who istraining the neural network to fully understand all the physical and/oroptical mechanisms which dictate how various aberrations impact thecritical dimension uniformity for various types of features.Alternatively, however, because the physical and/or optical mechanismsare understood, as evidenced by the lithography simulator 202, an expertsystem may alternatively be utilized which incorporates the physics andoptics principles of lithography as a set of rules which are applied tothe provided input data to determine an effective imaging systemaberration condition associated with the deviations of the criticaldimensions of various features from their intended nominal values.Therefore use of such an expert system or other form of intelligentsystem is also contemplated as falling with the scope of the presentinvention.

Returning now to FIG. 4, the method 250 continues and the trained orcalibrated neural network 208 is utilized at Step 264, by inputting theexperimental test feature critical dimension data from the memory 210,as illustrated in FIG. 11. Note that the experimental test feature datacritical dimension data includes the critical dimensions for variousfeatures ({tilde over (CD)}₁,{tilde over (CD)}₂, . . .{tilde over(CD)}_(n)) for a plurality of different points across the substrate 212((X₁,Y₁,)(X₂,Y₁) . . . (X_(i),Y_(j))). Likewise, the method 250 includesinputting the nominal critical dimension data for each of the featuresat Step 266. Since the nominal features are intended to be uniform ateach point across the substrate, only one set of nominal featurecritical dimension data needs to be input to the neural network 208.

Recall that the neural network 208 has been calibrated to determine or“extract” an effective imaging system aberration condition given adeviation between two pieces of critical dimension data (i.e., theexperimental and nominal feature critical dimension data). Therefore theneural network 208 outputs imaging system aberration conditions(preferably in the form of Zernike coefficients) for the variousdeviations for the various points 218. Because the imaging systemaberrations may differ across the image field, the deviations in theexperimental test feature critical dimension data from the nominalfeature critical dimension data also may differ at the various points218 across the substrate 212. Consequently, the output imaging systemaberration conditions (Z₁,Z₂. . . Z_(p)) differ for each point 218.(e.g., ((Z₁,X₁,Y₁,),(Z₂,X₁,Y₁) . . .(Z_(p),X₁,Y₁,);(Z₁,X₂,Y₁)(Z₂,X₂,Y₁), . . . (Z₁,X₁,Y₁);. . . (Z₁,X₁,Y_(j)),(Z₁,X₁,Y₁), .. . (Z_(p),X₁,Y₁))).

Thus, as seen above, the system 200 and the method 250 provide for acharacterization of the imaging system at each point 218 whichcorresponds to the entire image field. The above imaging systemcharacterization data may then be used in a lithography compensationsystem 350 to provide compensation (e.g., illumination compensation) tothe projection-type photolithography system which was characterized asdiscussed above, or via any other characterization method and thusimprove the uniformity of the critical dimensions of features, asillustrated in FIG. 12. The compensation system 350 includes thelithography simulator 202 and an illumination modification device 352which are operatively coupled together.

As discussed supra, the lithography simulator 202, as illustrated inFIGS. 12 and 13, is operable to simulate lithography processing for agiven set of processing conditions, imaging system aberrationconditions, nominal feature critical dimension data, and an illuminationscheme; and further is operable to output simulated feature criticaldimension data associated with the input conditions. The illuminationmodification device 352 according to a preferred embodiment of thepresent invention is operable to compare the simulated feature criticaldimension data to the nominal feature critical dimension data and modifyone or more portions of the illumination scheme in response to thecomparison, as will be described in greater detail infra.

A method 400 for identifying an illumination compensation scheme for acharacterized imaging system is illustrated in FIG. 14. The method 400includes initializing the illumination scheme to an initial value, forexample, a uniform illumination scheme, at step 402. Alternatively,other initial illumination schemes may be used as initial startingpoints as may be desired.

The method 400 continues at step 404, wherein a lithography simulationis run using the lithography simulator 202. The lithography simulator202 preferably uses fixed lithography process conditions associated withhow a projection-type lithography system will be run in production(e.g., dose, focus, equipment set-up, etc.) and also utilizes as anotherinput the characterization data associated with the imaging system. Forexample, as discussed supra, the system 200 of FIG. 3 may be used tocharacterize an imaging system and provide aberration characterizationdata in the form of Zernike coefficients. Alternatively, however, otherforms of characterization data may be utilized and are contemplated asfalling within the scope of the present invention. In addition, theinput data to the simulator 202 further includes the nominal featurecritical dimension data and the initial illumination scheme. Using theabove input data, the lithography simulator 202 provides an outputcontaining simulated feature critical dimension data that varies fromthe nominal feature critical dimension data at one or more points due tothe input imaging system aberrations (e.g., {overscore (CD)}₁(X₁, Y₁),{overscore (CD)}₂(X₁, Y₁), . . .{overscore (CD)}_(n)(X₁. . .Y₁); . . .{overscore (CD)}₁(X₁, Y₁), {overscore (CD)}₂ (X₁, Y_(j)), . . . .({overscore (CD)}_(n) (X_(i), Y_(j))), as illustrated in FIG. 13.

At step 406, the illumination modification device 352 compares thesimulated feature critical dimension data at the plurality of points 218to the nominal feature critical dimension data. Preferably, thecomparison is conducted one point 218 at a time (e.g., (X₁, Y₁), then(X₂, Y₁), etc.), however, the comparison of step 406 may alternativelybe performed for a plurality of points in parallel. If the simulatedfeature critical dimension data is within an acceptable range of thenominal feature critical dimension at that point at step 408 (YES), anacceptable illumination scheme for that point in the image field (e.g.,I(X_(i), Y_(j))) has been established at step 410 and no furthermodifications to the illumination scheme is performed for thatparticular point. If, however, the simulated feature critical dimensionsat that point are not within an acceptable range of the nominal featurecritical dimensions at step 408 (NO), the illumination scheme is thenmodified at that point at step 412.

The manner in which the illumination scheme is altered may vary.According to one exemplary embodiment of the present invention, theillumination scheme may be varied randomly to another, differingillumination scheme. Such a variation may include, for example, changingthe illumination intensity, phase or coherency (or a combinationthereof) of the illumination radiation. Alternatively, the simulatedfeature critical dimension data may be analyzed using one or more setsof rules which are based on the dependence of the critical dimensions offeatures on the illumination, and then identifies an illumination schememodification to compensate for the imaging system aberration conditionat that point Use of such an expert rules set (e.g., if-then-else rules)is contemplated as falling within the scope of the present invention.

After the illumination scheme is modified at step 412, anothersimulation is run at step 404 using the modified illumination scheme asa portion of the input data. The steps of analyzing the output data,determining whether additional illumination scheme modifications arenecessary and making any such modifications (steps 406, 408 and 412) arecontinued until an acceptable illumination scheme is established foreach of the points 218 corresponding to the various points in the imagefield. Once each point has an acceptable illumination scheme, the method400 is complete, and the illumination scheme modification device 352 ofFIG. 12, which preferably was being modified in software, can beconstructed in hardware for utilization in the lithography systememploying the imaging system which was characterized. In this manner,the non-uniformities in the critical dimensions of various features dueto the imaging system aberrations are substantially reduced.

FIG. 15 illustrates an exemplary, simplified perspective view of aprojection-type lithography system 500 incorporating an illuminationmodification filter 502 according to the present invention. Theillumination modification filter 502 preferably includes a plurality ofdistinct illumination filtering zones, wherein each zone is operable toprovide a distinct and unique illumination scheme. A simplifiedexemplary filter 502 having only three such illumination modificationzones 504 is illustrated in FIG. 16.

A first zone 504 a in the filter 502 illustrates an exemplary pupilfilter which inverts or otherwise varies the phase of light which passesthrough a central, circular region 506 with respect to the phase of thelight which passes through an annular region 508 surrounding the inner,circular region 506. As is well known by those skilled in the art, sucha phase inversion may be provided in either a continuous or step-wisefashion. In addition, as is well known by those skilled in the art, sucha pupil filter 504 a may be constructed by forming a transparentdielectric film over the central region 506, wherein the thickness ofthe film may be controlled to provide the desired phase variation.

A second zone 504 b in the filter 502 illustrates another exemplarypupil filter, wherein an inner region 510 exhibits a lower transmittancethan an outer, annular region 512 via a light-absorbing layer formed inthe inner region 510. The transmittance may be modified further byadjusting a radius of the inner region 510 as well as its degree oftransmittance. In so doing, a wide variation of illumination schemes maybe effectuated therewith. As is well known by those skilled in the art,such a variable transmittance pupil filter 504 b may be formed byforming a light-absorbing material, such as a metal film, over thecentral region 510, wherein a thickness of the material may becontrolled to vary its transmittance.

The last zone 504 c illustrates an exemplary uniform illumination pupilfilter. Such a pupil filter is operable to allow all or some of theillumination light to transmit therethrough in a uniform fashion. Such afilter 504 c may be constructed with a uniform, transparent plate 514 oralternatively may have a uniform light-absorbing material thereon havinga thickness which provides for a uniform illumination attenuation.Although three exemplary pupil filters are illustrated in FIG. 16 anddiscussed above, other type pupil filters may be utilized and arecontemplated as falling within the scope of the present invention. Inaddition, the various functions described above, such as phase variationor transmittance attenuation, may be combined as may be desired. Inaddition, although the filter 502 of FIG. 16 is shown with only threefiltering zones 504, the filter will preferably include a substantiallygreater number of zones, for example, a number equal to the number ofpoints 218 on the substrate so that each point may have its own customillumination zone which may be modified with respect to neighboringzones.

In the above manner, the present invention provides for a customillumination scheme across the image field which provides illuminationcompensation to thereby compensate for imaging system aberrations.Consequently, the uniformity of critical dimensions of features isimproved across the substrate.

Although the invention has been shown and described with respect to acertain preferred embodiment or embodiments, it is obvious thatequivalent alterations and modifications will occur to others skilled inthe art upon the reading and understanding of this specification and theannexed drawings. In particular regard to the various functionsperformed by the above described components (assemblies, devices,circuits, etc.), the terms (including a reference to a “means”) used todescribe such components are intended to correspond, unless otherwiseindicated, to any component which performs the specified function of thedescribed component (i.e., that is functionally equivalent), even thoughnot structurally equivalent to the disclosed structure which performsthe function in the herein illustrated exemplary embodiments of theinvention. In addition, while a particular feature of the invention mayhave been disclosed with respect to only one of several embodiments,such feature may be combined with one or more other features of theother embodiments as may be desired and advantageous for any given orparticular application.

What is claimed is:
 1. A method of extracting effective imaging systemaberrations from test data collected from test structures constructedfrom a lithography system having an imaging system associated therewith,comprising the steps of: inputting experimental critical dimension datato an intelligent system, wherein the experimental critical dimensiondata corresponds to fabricated features on a substrate at a plurality ofpoints in an image field of the imaging system; inputting nominalcritical dimension data to the intelligent system, wherein the nominalcritical dimension data corresponds to the fabricated features on thesubstrate at a plurality of points in an image field of the imagingsystem; and determining the effective aberrations across the image fieldof the imaging system associated with the lithography system used tofabricate the features using the intelligent system.
 2. The method ofclaim 1, wherein the intelligent system comprises one of a neuralnetwork and an expert system.
 3. The method of claim 1, wherein theexperimental critical dimension data is collected by measuring thefabricated features on the substrate using one of an electricallinewidth measurement technique, an optical measurement technique and ascanning electron microscope.
 4. The method of claim 1, wherein theexperimental critical dimension data represents fabricated features at aplurality of points across the substrate which correspond to a pluralityof points in an image field of the imaging system.
 5. The method ofclaim 1, wherein the fabricated features comprise an array of featureswhich are duplicated at a plurality of points across the substrate. 6.The method of claim 1, wherein the intelligent system comprises acalibrated neural network and wherein calibrating the neural networkcomprises the steps of: (a) inputting an imaging system aberrationcondition to a lithography simulator; (b) performing a lithographysimulation for a plurality of input features representing a set ofnominal critical dimensions at a fixed set of lithography processconditions and at the imaging system aberration condition; (c) saving anoutput of the lithography simulation of step (b) in a memory, whereinthe output comprises a plurality of simulated features corresponding tothe input features having critical dimensions that differ from thenominal critical dimensions due to the aberration condition; (d)inputting another imaging system aberration condition to the lithographysimulator; and (e) repeating steps (b), (c) and (d) a predeterminednumber of times, thereby generating and saving in the memory a trainingset of data representing critical dimensions of simulated featurescorresponding to the input features at a plurality of imaging systemaberration conditions.
 7. A method of extracting effective imagingsystem aberrations from test data collected from test structuresconstructed from a lithography system having an imaging systemassociated therewith, comprising the steps of: inputting experimentalcritical dimension data corresponding to fabricated features on asubstrate to an intelligent system; inputting nominal critical dimensiondata corresponding to the fabricated features on the substrate to theintelligent system; and determining the effective aberrations of theimaging system associated with the lithography system used to fabricatethe features using a calibrated neural network, and wherein calibratingthe neural network comprises the steps of: (a) inputting an imagingsystem aberration condition to a lithography simulator; (b) performing alithography simulation for a plurality of input features representing aset of nominal critical dimensions at a fixed set of lithography processconditions and at the imaging system aberration condition: (c) saving anoutput of the lithography simulation of step (b) in a memory, whereinthe output comprises a plurality of simulated features corresponding tothe input features having critical dimensions that differ from thenominal critical dimensions due to the aberration condition; (d)inputting another imaging system aberration condition to the lithographysimulator; and (e) repeating steps (b), (c) and (d) a predeterminednumber of times, thereby generating and saving in the memory a trainingset of data representing critical dimensions of simulated featurescorresponding to the input features at a plurality of imaging systemaberration conditions; (f) inputting the simulated features to theneural network; (g) inputting the input features to the neural network;(h) comparing an output of the neural network, which represents animaging system aberration condition, with an expected imaging systemaberration condition using the training set; (i) adjusting one or moreinterconnections of nodes within the neural network in response to thecomparison; and (j) repeating steps (h) and (i) until the output iswithin a predetermined amount of the expected imaging system aberrationcondition.
 8. The method of claim 7, wherein adjusting the one or moreinterconnections comprises adjusting a weight of the one or moreinterconnections of nodes within the neural network.
 9. The method ofclaim 8, wherein the adjustment of the weight of the one or moreinterconnections of nodes is based on a Levenberg-Marquardt trainingalgorithm.
 10. A system for characterizing an imaging system for aprojection-type photolithography system, comprising: a lithographysimulator, wherein the lithography simulator is operable to simulate alithography process step for one or more nominal features based on a setof lithography process conditions and one or more imaging systemaberration conditions, and wherein the lithography simulator outputs oneor more simulated features which vary with respect to the nominalfeatures due to the imaging system aberration conditions; a first memoryoperatively coupled to the lithography simulator, wherein the memorystores input data and output data associated with one or morelithography simulations performed by the lithography simulator; a secondmemory which stores experimental test feature critical dimension datafor one or more experimental test features which were formed using theimaging system being characterized; and an intelligent systemoperatively coupled to the first memory and the second memory, whereinthe intelligent system receives the input data and the output dataassociated with the one or more lithography simulations and theexperimental test feature critical dimension data and determines aneffective imaging system aberration condition at one or more points inresponse thereto.
 11. The system of claim 10, wherein the first memoryand the second memory are different memories.
 12. The system of claim10, wherein the intelligent system comprises an expert system.
 13. Thesystem of claim 10, wherein the intelligent system comprises a neuralnetwork.
 14. A system for characterizing an imaging system for aprojection-type photolithography system, comprising: a lithographysimulator, wherein the lithography simulator is operable to simulate alithography process step for one or more nominal features based on a setof lithography process conditions and one or more imaging systemaberration conditions, and wherein the lithography simulator outputs oneor more simulated features which vary with respect to the nominalfeatures due to the imaging system aberration conditions; a first memoryoperatively coupled to the lithography simulator, wherein the memorystores input data and output data associated with one or morelithography simulations performed by the lithography simulator; a secondmemory which stores experimental test feature critical dimension datafor one or more experimental test features which were formed using theimaging system being characterized; and an expert system operativelycoupled to the first memory and the second memory, wherein theintelligent system receives the input data and the output dataassociated with the one or more lithography simulations and theexperimental test feature critical dimension data and determines aneffective imaging system aberration condition at one or more points inresponse thereto, wherein the expert system contains a plurality ofexpert rules which are applied to the input data, output data and theexperimental test feature critical dimension data to determine theeffective imaging system aberration condition at the one or more points.15. A system for characterizing an imaging system for a projection-typephotolithography system, comprising: a lithography simulator, whereinthe lithography simulator is operable to simulate a lithography processstep for one or more nominal features based on a set of lithographyprocess conditions and one or more imaging system aberration conditions,and wherein the lithography simulator outputs one or more simulatedfeatures which vary with respect to the nominal features due to theimaging system aberration conditions; a first memory operatively coupledto the lithography simulator, wherein the memory stores input data andoutput data associated with one or more lithography simulationsperformed by the lithography simulator; a second memory which storesexperimental test feature critical dimension data for one or moreexperimental test features which were formed using the imaging systembeing characterized; and a neural network operatively coupled to thefirst memory and the second memory, wherein the intelligent systemreceives the input data and the output data associated with the one ormore lithography simulations and the experimental test feature criticaldimension data and determines an effective imaging system aberrationcondition at one or more points in response thereto, and wherein theintelligent system comprises a neural network, wherein the neuralnetwork uses the input data and the output data associated with the oneor more lithography simulations as a training set to calibrate theneural network and thereby establish a transfer function which maps adeviation between one or more nominal features and the one or moreexperimental test features to an imaging system aberration conditionwhich could have produced such deviation.
 16. The system of claim 15,wherein the calibrated neural network uses the experimental test featurecritical dimension data and data related to one or more nominal featureswhich correspond to the experimental test features to determine theimaging system aberration condition at the one or more points.